TY - GEN
T1 - Locating using prior information
T2 - ACM SIGCOMM 2013 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, SIGCOMM 2013
AU - Chen, Yuanfang
AU - Crespi, Noel
AU - Lv, Lin
AU - Li, Mingchu
AU - Ortiz, Antonio M.
AU - Shu, Lei
PY - 2013/8/12
Y1 - 2013/8/12
N2 - Most indoor localization algorithms are based on Received Signal Strength (RSS), in which RSS signatures of an interested area are annotated with their real recorded locations. However, according to our experiments, RSS signatures are not suitable as the unique annotations (like Fingerprints) of recorded locations. In this study, we investigate the characteristics of RSS (e.g., how the RSS values change as time goes on and between consecutive positions?). On this basis, we design LuPI (Locating using Prior Information) that exploits the characteristics of RSS: with user motion, LuPI uses novel sensors integrated in smartphones to construct the RSS variation space (like radio map) of a floor plan as prior information. The deployment of LuPI is easy and rapid since little human intervention is needed. In LuPI, the calibration of "radio map" is crowd-sourced, automatic and scheduled. Experimental results show that LuPI achieves comparable location accuracy to previous approaches, even without the statistical information of site survey.
AB - Most indoor localization algorithms are based on Received Signal Strength (RSS), in which RSS signatures of an interested area are annotated with their real recorded locations. However, according to our experiments, RSS signatures are not suitable as the unique annotations (like Fingerprints) of recorded locations. In this study, we investigate the characteristics of RSS (e.g., how the RSS values change as time goes on and between consecutive positions?). On this basis, we design LuPI (Locating using Prior Information) that exploits the characteristics of RSS: with user motion, LuPI uses novel sensors integrated in smartphones to construct the RSS variation space (like radio map) of a floor plan as prior information. The deployment of LuPI is easy and rapid since little human intervention is needed. In LuPI, the calibration of "radio map" is crowd-sourced, automatic and scheduled. Experimental results show that LuPI achieves comparable location accuracy to previous approaches, even without the statistical information of site survey.
KW - floor plan
KW - indoor localization
KW - smart devices
KW - wireless networks
UR - https://www.scopus.com/pages/publications/84883304180
U2 - 10.1145/2486001.2491688
DO - 10.1145/2486001.2491688
M3 - Conference contribution
AN - SCOPUS:84883304180
SN - 9781450320566
T3 - SIGCOMM 2013 - Proceedings of the ACM SIGCOMM 2013 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
SP - 463
EP - 464
BT - SIGCOMM 2013 - Proceedings of the ACM SIGCOMM 2013 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
PB - Association for Computing Machinery
Y2 - 12 August 2013 through 16 August 2013
ER -